Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments

The current research intents on enhancing the service ability of mobile robot by cooperative path planning. The strategy is developed by fusion of sine cosine algorithm and particle swarm optimization approach for the transition of service robot in complex environment. To ensure the successful execution of the intended task it is essential to have a faultless and collision-less path for the mobile robot. This supposition can be achieved by producing an intelligent fault-managed approach. The proposed paper addresses the object transportation by a pair of robots from source to destination, this task can be accomplished in three step, such as fault identification, fault resolution using robot replacement and computation of a collision-less path. At each step of transition, path planning is carried out to reach the target location. Sine cosine algorithm improves the exploration capability of the robots in the multi robot environment. Particle swarm optimization being the simplest technique to exercise the path planning problem produces an optimal global position for each particle along each dimension. The fusion of both provides a balance exploration-exploitation ability of the mobile robot. Fault identification overcomes the faulty transition and unsuccessful transition of the robot by detecting the fault of any robot through its non-responding time. K- nearest neighbor approach identifies the nearest working robot to replace the faulty on. The algorithm has been exercised in C language to showcase its capability in terms of execution time, path traveled, path deviated etc. The comparative analysis proofs the supremacy of the proposed algorithm in terms of several metrics such as path planning, cooperation, fault management, etc.

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Cooperative Path Planning for Object Transportation with Fault Management

K,

Sahu,

Das

et al. 2023

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The current research intents on enhancing the service ability of mobile robot by cooperative path planning. The strategy is developed by fusion of sine cosine algorithm and particle swarm optimization approach for the transition of service robot in complex environment. To ensure the successful execution of the intended task it is essential to have a faultless and collision-less path for the mobile robot. This supposition can be achieved by producing an intelligent fault-managed approach. The proposed paper addresses the object transportation by a pair of robots from source to destination, this task can be accomplished in three step, such as fault identification, fault resolution using robot replacement and computation of a collision-less path. At each step of transition, path planning is carried out to reach the target location. Sine cosine algorithm improves the exploration capability of the robots in the multi robot environment. Particle swarm optimization being the simplest technique to exercise the path planning problem produces an optimal global position for each particle along each dimension. The fusion of both provides a balance exploration-exploitation ability of the mobile robot. Fault identification overcomes the faulty transition and unsuccessful transition of the robot by detecting the fault of any robot through its non-responding time. K- nearest neighbor approach identifies the nearest working robot to replace the faulty on. The algorithm has been exercised in C language to showcase its capability in terms of execution time, path traveled, path deviated etc. The comparative analysis proofs the supremacy of the proposed algorithm in terms of several metrics such as path planning, cooperation, fault management, etc.

show abstract

Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments

Cited by 4 publications

References 27 publications

Study on the Use of the Ant Colony Algorithm and Genetic Algorithm for Travelling Salesman Problem Path Planning

Study on the Use of the Ant Colony Algorithm and Genetic Algorithm for Travelling Salesman Problem Path Planning

Cooperative Path Planning for Object Transportation with Fault Management

Cooperative Path Planning for Object Transportation with Fault Management

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